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Bias in the Context of Artificial Intelligence Systems: Analyzing the risks and contributors from a data perspective

Firera Colmenares, Stefany Del Carmen LU and Vakil, Mana LU (2022) INFM10 20221
Department of Informatics
Abstract
As Artificial Intelligence (AI) is progressing to take over decision making in different industries, the threat that comes with the use of these systems is also increasing. One major threat is the risk of these systems acting biased, causing discrimination to parts of the population. To tackle the risk of AI systems acting biased it is important to understand how these biases originate in the first place. An analysis is made to understand where in the process the risk of bias takes place as well as highlighting the major contributor to bias in AI systems. Through a qualitative approach, practitioners currently working with AI and data were inter-viewed and presented with real-life examples of AI systems acting biased to help identify the... (More)
As Artificial Intelligence (AI) is progressing to take over decision making in different industries, the threat that comes with the use of these systems is also increasing. One major threat is the risk of these systems acting biased, causing discrimination to parts of the population. To tackle the risk of AI systems acting biased it is important to understand how these biases originate in the first place. An analysis is made to understand where in the process the risk of bias takes place as well as highlighting the major contributor to bias in AI systems. Through a qualitative approach, practitioners currently working with AI and data were inter-viewed and presented with real-life examples of AI systems acting biased to help identify the reasons for the biased outcomes in these AI systems. The findings in this thesis indicate that data is a major contributor to bias in these systems, however, research has mostly been attributed to algorithms. Conclusively, this thesis found that there is a high risk of bias in the data collection, data preparation and model development stages in the AI systems. (Less)
Please use this url to cite or link to this publication:
author
Firera Colmenares, Stefany Del Carmen LU and Vakil, Mana LU
supervisor
organization
course
INFM10 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
bias, data bias, artificial intelligence, risk of bias, algorithmic bias
report number
INF22-18
language
English
id
9091994
date added to LUP
2022-09-07 12:55:47
date last changed
2022-09-07 12:55:47
@misc{9091994,
  abstract     = {{As Artificial Intelligence (AI) is progressing to take over decision making in different industries, the threat that comes with the use of these systems is also increasing. One major threat is the risk of these systems acting biased, causing discrimination to parts of the population. To tackle the risk of AI systems acting biased it is important to understand how these biases originate in the first place. An analysis is made to understand where in the process the risk of bias takes place as well as highlighting the major contributor to bias in AI systems. Through a qualitative approach, practitioners currently working with AI and data were inter-viewed and presented with real-life examples of AI systems acting biased to help identify the reasons for the biased outcomes in these AI systems. The findings in this thesis indicate that data is a major contributor to bias in these systems, however, research has mostly been attributed to algorithms. Conclusively, this thesis found that there is a high risk of bias in the data collection, data preparation and model development stages in the AI systems.}},
  author       = {{Firera Colmenares, Stefany Del Carmen and Vakil, Mana}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Bias in the Context of Artificial Intelligence Systems: Analyzing the risks and contributors from a data perspective}},
  year         = {{2022}},
}